Tracing the transition of methicillin resistance in sub-populations of Staphylococcus aureus, using SELDI-TOF Mass Spectrometry and Artificial Neural Network Analysis
Strains ( n = 99) of Staphylococcus aureus isolated from a large number of clinical sources and tested for methicillin sensitivity were analysed by MALDI-TOF-MS using the Weak Cation Exchange (CM10) ProteinChip Array (designated SELDI-TOF-MS). The profile data generated was analysed using Artificial...
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Veröffentlicht in: | Systematic and applied microbiology 2011-02, Vol.34 (1), p.81-86 |
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creator | Shah, Haroun N. Rajakaruna, Lakshani Ball, Graham Misra, Raju Al-Shahib, Ali Fang, Min Gharbia, Saheer E. |
description | Strains (
n
=
99) of
Staphylococcus aureus isolated from a large number of clinical sources and tested for methicillin sensitivity were analysed by MALDI-TOF-MS using the Weak Cation Exchange (CM10) ProteinChip Array (designated SELDI-TOF-MS). The profile data generated was analysed using Artificial Neural Network (ANN) Analysis modelling techniques. Seven key ions identified by the ANNs that were predictive of MRSA and MSSA were validated by incorporation into a model. This model exhibited an area under the ROC curve value of 0.9147 indicating the potential application of this approach for rapidly characterising MRSA and MSSA isolates. Nearly all strains (
n
=
97) were correctly assigned to the correct group, with only two aberrant MSSA strains being misclassified. However, approximately 21% of the strains appeared to be in a process of transition as resistance to methicillin was being acquired. |
doi_str_mv | 10.1016/j.syapm.2010.11.002 |
format | Article |
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Staphylococcus aureus isolated from a large number of clinical sources and tested for methicillin sensitivity were analysed by MALDI-TOF-MS using the Weak Cation Exchange (CM10) ProteinChip Array (designated SELDI-TOF-MS). The profile data generated was analysed using Artificial Neural Network (ANN) Analysis modelling techniques. Seven key ions identified by the ANNs that were predictive of MRSA and MSSA were validated by incorporation into a model. This model exhibited an area under the ROC curve value of 0.9147 indicating the potential application of this approach for rapidly characterising MRSA and MSSA isolates. Nearly all strains (
n
=
97) were correctly assigned to the correct group, with only two aberrant MSSA strains being misclassified. However, approximately 21% of the strains appeared to be in a process of transition as resistance to methicillin was being acquired.</description><identifier>ISSN: 0723-2020</identifier><identifier>EISSN: 1618-0984</identifier><identifier>DOI: 10.1016/j.syapm.2010.11.002</identifier><identifier>PMID: 21257279</identifier><language>eng</language><publisher>Germany: Elsevier GmbH</publisher><subject>ANN ; Bacterial Proteins - analysis ; CM10 ; Drug Resistance, Bacterial ; Methicillin-Resistant Staphylococcus aureus - chemistry ; Methicillin-Resistant Staphylococcus aureus - classification ; MRSA ; MSSA ; Neural Networks (Computer) ; Protein Array Analysis - methods ; SELDI-TOF-MS ; Sensitivity and Specificity ; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods ; Staphylococcus aureus</subject><ispartof>Systematic and applied microbiology, 2011-02, Vol.34 (1), p.81-86</ispartof><rights>2010 Elsevier GmbH</rights><rights>Copyright © 2010 Elsevier GmbH. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c414t-8e796c27243802a0d12b92bbfedb4029d690e4e7a032eca9c43c9e07297545783</citedby><cites>FETCH-LOGICAL-c414t-8e796c27243802a0d12b92bbfedb4029d690e4e7a032eca9c43c9e07297545783</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.syapm.2010.11.002$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21257279$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shah, Haroun N.</creatorcontrib><creatorcontrib>Rajakaruna, Lakshani</creatorcontrib><creatorcontrib>Ball, Graham</creatorcontrib><creatorcontrib>Misra, Raju</creatorcontrib><creatorcontrib>Al-Shahib, Ali</creatorcontrib><creatorcontrib>Fang, Min</creatorcontrib><creatorcontrib>Gharbia, Saheer E.</creatorcontrib><title>Tracing the transition of methicillin resistance in sub-populations of Staphylococcus aureus, using SELDI-TOF Mass Spectrometry and Artificial Neural Network Analysis</title><title>Systematic and applied microbiology</title><addtitle>Syst Appl Microbiol</addtitle><description>Strains (
n
=
99) of
Staphylococcus aureus isolated from a large number of clinical sources and tested for methicillin sensitivity were analysed by MALDI-TOF-MS using the Weak Cation Exchange (CM10) ProteinChip Array (designated SELDI-TOF-MS). The profile data generated was analysed using Artificial Neural Network (ANN) Analysis modelling techniques. Seven key ions identified by the ANNs that were predictive of MRSA and MSSA were validated by incorporation into a model. This model exhibited an area under the ROC curve value of 0.9147 indicating the potential application of this approach for rapidly characterising MRSA and MSSA isolates. Nearly all strains (
n
=
97) were correctly assigned to the correct group, with only two aberrant MSSA strains being misclassified. However, approximately 21% of the strains appeared to be in a process of transition as resistance to methicillin was being acquired.</description><subject>ANN</subject><subject>Bacterial Proteins - analysis</subject><subject>CM10</subject><subject>Drug Resistance, Bacterial</subject><subject>Methicillin-Resistant Staphylococcus aureus - chemistry</subject><subject>Methicillin-Resistant Staphylococcus aureus - classification</subject><subject>MRSA</subject><subject>MSSA</subject><subject>Neural Networks (Computer)</subject><subject>Protein Array Analysis - methods</subject><subject>SELDI-TOF-MS</subject><subject>Sensitivity and Specificity</subject><subject>Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods</subject><subject>Staphylococcus aureus</subject><issn>0723-2020</issn><issn>1618-0984</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkc9u1DAQhyMEokvhCZDANy5ksZ0_jg8cVqWFSgs97PZsOc6k6yWJU48DygvxnDjdwhFOI1vf_MaeL0leM7pmlJUfjmuc9divOV1u2JpS_iRZsZJVKZVV_jRZUcGzlFNOz5IXiEdKWS5L9jw544wXggu5Sn7tvTZ2uCPhACR4PaAN1g3EtaSHcLDGdp0diAe0GPRggMQTTnU6unHq9MLiAu-CHg9z54wzZkKiJw8TvicTLtm7y-2n63R_c0W-akSyG8EE72K-n4keGrLxwbZxlO7IN5j8Qwk_nf9ONoPu5jj6ZfKs1R3Cq8d6ntxeXe4vvqTbm8_XF5ttanKWh7QCIUvDBc-zinJNG8Zryeu6habOKZdNKSnkIDTNOBgtTZ4ZCXFNUhR5IarsPHl3yh29u58Ag-otGug6PYCbUEkqWMFLVv6XrIqM80IWIpLZiTTeIXpo1ehtr_2sGFWLSXVUDybVYlIxpqLJ2PXmMX-qe2j-9vxRF4G3J6DVTuk7b1Hd7mJCGTWLMqfLZz6eCIgb-2HBKzQWosPG-mhANc7-8wm_ARwPvAs</recordid><startdate>20110201</startdate><enddate>20110201</enddate><creator>Shah, Haroun N.</creator><creator>Rajakaruna, Lakshani</creator><creator>Ball, Graham</creator><creator>Misra, Raju</creator><creator>Al-Shahib, Ali</creator><creator>Fang, Min</creator><creator>Gharbia, Saheer E.</creator><general>Elsevier GmbH</general><scope>FBQ</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7QL</scope><scope>C1K</scope></search><sort><creationdate>20110201</creationdate><title>Tracing the transition of methicillin resistance in sub-populations of Staphylococcus aureus, using SELDI-TOF Mass Spectrometry and Artificial Neural Network Analysis</title><author>Shah, Haroun N. ; Rajakaruna, Lakshani ; Ball, Graham ; Misra, Raju ; Al-Shahib, Ali ; Fang, Min ; Gharbia, Saheer E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c414t-8e796c27243802a0d12b92bbfedb4029d690e4e7a032eca9c43c9e07297545783</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>ANN</topic><topic>Bacterial Proteins - analysis</topic><topic>CM10</topic><topic>Drug Resistance, Bacterial</topic><topic>Methicillin-Resistant Staphylococcus aureus - chemistry</topic><topic>Methicillin-Resistant Staphylococcus aureus - classification</topic><topic>MRSA</topic><topic>MSSA</topic><topic>Neural Networks (Computer)</topic><topic>Protein Array Analysis - methods</topic><topic>SELDI-TOF-MS</topic><topic>Sensitivity and Specificity</topic><topic>Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods</topic><topic>Staphylococcus aureus</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shah, Haroun N.</creatorcontrib><creatorcontrib>Rajakaruna, Lakshani</creatorcontrib><creatorcontrib>Ball, Graham</creatorcontrib><creatorcontrib>Misra, Raju</creatorcontrib><creatorcontrib>Al-Shahib, Ali</creatorcontrib><creatorcontrib>Fang, Min</creatorcontrib><creatorcontrib>Gharbia, Saheer E.</creatorcontrib><collection>AGRIS</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Environmental Sciences and Pollution Management</collection><jtitle>Systematic and applied microbiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shah, Haroun N.</au><au>Rajakaruna, Lakshani</au><au>Ball, Graham</au><au>Misra, Raju</au><au>Al-Shahib, Ali</au><au>Fang, Min</au><au>Gharbia, Saheer E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Tracing the transition of methicillin resistance in sub-populations of Staphylococcus aureus, using SELDI-TOF Mass Spectrometry and Artificial Neural Network Analysis</atitle><jtitle>Systematic and applied microbiology</jtitle><addtitle>Syst Appl Microbiol</addtitle><date>2011-02-01</date><risdate>2011</risdate><volume>34</volume><issue>1</issue><spage>81</spage><epage>86</epage><pages>81-86</pages><issn>0723-2020</issn><eissn>1618-0984</eissn><abstract>Strains (
n
=
99) of
Staphylococcus aureus isolated from a large number of clinical sources and tested for methicillin sensitivity were analysed by MALDI-TOF-MS using the Weak Cation Exchange (CM10) ProteinChip Array (designated SELDI-TOF-MS). The profile data generated was analysed using Artificial Neural Network (ANN) Analysis modelling techniques. Seven key ions identified by the ANNs that were predictive of MRSA and MSSA were validated by incorporation into a model. This model exhibited an area under the ROC curve value of 0.9147 indicating the potential application of this approach for rapidly characterising MRSA and MSSA isolates. Nearly all strains (
n
=
97) were correctly assigned to the correct group, with only two aberrant MSSA strains being misclassified. However, approximately 21% of the strains appeared to be in a process of transition as resistance to methicillin was being acquired.</abstract><cop>Germany</cop><pub>Elsevier GmbH</pub><pmid>21257279</pmid><doi>10.1016/j.syapm.2010.11.002</doi><tpages>6</tpages></addata></record> |
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subjects | ANN Bacterial Proteins - analysis CM10 Drug Resistance, Bacterial Methicillin-Resistant Staphylococcus aureus - chemistry Methicillin-Resistant Staphylococcus aureus - classification MRSA MSSA Neural Networks (Computer) Protein Array Analysis - methods SELDI-TOF-MS Sensitivity and Specificity Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods Staphylococcus aureus |
title | Tracing the transition of methicillin resistance in sub-populations of Staphylococcus aureus, using SELDI-TOF Mass Spectrometry and Artificial Neural Network Analysis |
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